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<table width="100%"><tr><td>gamlss.fp(gamlss)</td><td align="right">R Documentation</td></tr></table><object type="application/x-oleobject" classid="clsid:1e2a7bd0-dab9-11d0-b93a-00c04fc99f9e">
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<h2>Support for Function fp()</h2>


<h3>Description</h3>

<p>
Those are support for the functions <code>fp()</code> and <code>pp</code>.
It is not intended to be called directly by users.
</p>


<h3>Usage</h3>

<pre>
gamlss.fp(x, y, w, npoly = 2, xeval = NULL)
gamlss.pp(x, y, w)
</pre>


<h3>Arguments</h3>

<table summary="R argblock">
<tr valign="top"><td><code>x</code></td>
<td>
the <code>x</code> for function <code>gamlss.fp</code> is referred to the design matric of the specific parameter model (not to be used by the user)</td></tr>
<tr valign="top"><td><code>y</code></td>
<td>
the <code>y</code> for function <code>gamlss.fp</code> is referred to the working variable of the specific parameter model (not to be used by the user)</td></tr>
<tr valign="top"><td><code>w</code></td>
<td>
the <code>w</code> for function <code>gamlss.fp</code> is referred to the iterative weight variable of the specific parameter model (not to be used by the user) </td></tr>
<tr valign="top"><td><code>npoly</code></td>
<td>
a positive indicating how many fractional polynomials should be considered in the fit. Can take the values 1, 2 or 3 with 2 as default </td></tr>
<tr valign="top"><td><code>xeval</code></td>
<td>
used in prediction </td></tr>
</table>

<h3>Value</h3>

<p>
Returns a list with
</p>
<table summary="R argblock">
<tr valign="top"><td><code>fitted.values</code></td>
<td>
fitted</td></tr>
<tr valign="top"><td><code>residuals</code></td>
<td>
residuals</td></tr>
<tr valign="top"><td><code>var</code></td>
<td>
</td></tr>
<tr valign="top"><td><code>nl.df</code></td>
<td>
the trace of the smoothing matrix</td></tr>
<tr valign="top"><td><code>lambda</code></td>
<td>
the value of the smoothing parameter</td></tr>
<tr valign="top"><td><code>coefSmo</code></td>
<td>
the coefficients from the smoothing fit</td></tr>
<tr valign="top"><td><code>varcoeff</code></td>
<td>
the variance of the coefficients</td></tr>
</table>

<h3>Author(s)</h3>

<p>
Mikis Stasinopoulos <a href="mailto:d.stasinopoulos@londonmet.ac.uk">d.stasinopoulos@londonmet.ac.uk</a>, Bob Rigby <a href="mailto:r.rigby@londonmet.ac.uk">r.rigby@londonmet.ac.uk</a>
</p>


<h3>References</h3>

<p>
Rigby, R. A. and  Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion), 
<EM>Appl. Statist.</EM>, <B>54</B>, part 3, pp 507-554.
</p>
<p>
Stasinopoulos D. M., Rigby R.A. and Akantziliotou C. (2006) Instructions on how to use the GAMLSS package in R.
Accompanying documentation in the current GAMLSS  help files, (see also  <a href="http://www.gamlss.com/">http://www.gamlss.com/</a>). 
</p>
<p>
Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R.
<EM>Journal of Statistical Software</EM>, Vol. <B>23</B>, Issue 7, Dec 2007, <a href="http://www.jstatsoft.org/v23/i07">http://www.jstatsoft.org/v23/i07</a>.
</p>


<h3>See Also</h3>

<p>
<code><a href="gamlss.html">gamlss</a></code>, <code><a href="bfp.html">fp</a></code>
</p>



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